Authorship enhanced corpus ingestion for natural language processing

ABSTRACT

Mechanisms for processing a corpus of information in a natural language processing system are provided. A corpus of information to process is identified and a set of author profiles associated with the corpus of information is retrieved. A content profile is generated for a portion of content of the corpus of information and the content profile is compared to the set of author profiles to generate an association of the content profile with at least one author profile in the set of author profiles. In addition, a processing operation of the natural language processing (NLP) system is controlled based on the association of the content profile with the at least one author profile.

This application is a continuation of application Ser. No. 14/012,337,filed Aug. 28, 2013, status awaiting publication.

BACKGROUND

The present application relates generally to an improved data processingapparatus and method and more specifically to mechanisms for performingauthorship enhanced corpus ingestion for natural language processing.

With the increased usage of computing networks, such as the Internet,humans are currently inundated and overwhelmed with the amount ofinformation available to them from various structured and unstructuredsources. However, information gaps abound as users try to piece togetherwhat they can find that they believe to be relevant during searches forinformation on various subjects. To assist with such searches, recentresearch has been directed to generating Question and Answer (QA)systems which may take an input question, analyze it, and return resultsindicative of the most probable answer to the input question. QA systemsprovide automated mechanisms for searching through large sets of sourcesof content, e.g., electronic documents, and analyze them with regard toan input question to determine an answer to the question and aconfidence measure as to how accurate an answer is for answering theinput question.

One such QA system is the Watson™ system available from InternationalBusiness Machines (IBM) Corporation of Armonk, N.Y. The Watson™ systemis an application of advanced natural language processing, informationretrieval, knowledge representation and reasoning, and machine learningtechnologies to the field of open domain question answering. The Watson™system is built on IBM's DeepQA™ technology used for hypothesisgeneration, massive evidence gathering, analysis, and scoring. DeepQA™takes an input question, analyzes it, decomposes the question intoconstituent parts, generates one or more hypothesis based on thedecomposed question and results of a primary search of answer sources,performs hypothesis and evidence scoring based on a retrieval ofevidence from evidence sources, performs synthesis of the one or morehypothesis, and based on trained models, performs a final merging andranking to output an answer to the input question along with aconfidence measure.

Various United States patent application Publications describe varioustypes of question and answer systems. U.S. Patent ApplicationPublication No. 2011/0125734 discloses a mechanism for generatingquestion and answer pairs based on a corpus of data. The system startswith a set of questions and then analyzes the set of content to extractanswer to those questions. U.S. Patent Application Publication No.2011/0066587 discloses a mechanism for converting a report of analyzedinformation into a collection of questions and determining whetheranswers for the collection of questions are answered or refuted from theinformation set. The results data are incorporated into an updatedinformation model.

SUMMARY

In one illustrative embodiment, a method, in a data processing systemcomprising a processor and a memory, for processing a corpus ofinformation in a natural language processing system. The methodcomprises identifying, by the data processing system, a corpus ofinformation to process and retrieving, by the data processing system, aset of author profiles associated with the corpus of information. Themethod further comprises generating, by the data processing system, acontent profile for a portion of content of the corpus of informationand comparing, by the data processing system, the content profile to theset of author profiles to generate an association of the content profilewith at least one author profile in the set of author profiles. Inaddition, the method comprises controlling a processing operation of thenatural language processing (NLP) system based on the association of thecontent profile with the at least one author profile.

In other illustrative embodiments, a computer program product comprisinga computer useable or readable medium having a computer readable programis provided. The computer readable program, when executed on a computingdevice, causes the computing device to perform various ones of, andcombinations of, the operations outlined above with regard to the methodillustrative embodiment.

In yet another illustrative embodiment, a system/apparatus is provided.The system/apparatus may comprise one or more processors and a memorycoupled to the one or more processors. The memory may compriseinstructions which, when executed by the one or more processors, causethe one or more processors to perform various ones of, and combinationsof, the operations outlined above with regard to the method illustrativeembodiment.

These and other features and advantages of the present invention will bedescribed in, or will become apparent to those of ordinary skill in theart in view of, the following detailed description of the exampleembodiments of the present invention.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The invention, as well as a preferred mode of use and further objectivesand advantages thereof, will best be understood by reference to thefollowing detailed description of illustrative embodiments when read inconjunction with the accompanying drawings, wherein:

FIG. 1 is an example diagram of a distributed data processing system inwhich aspects of the illustrative embodiments may be implemented;

FIG. 2 is an example block diagram of a computing device in whichaspects of the illustrative embodiments may be implemented;

FIG. 3 is an example block diagram of a question and answer (QA) systempipeline in which aspects of the illustrative embodiments may beimplemented;

FIG. 4 is an example diagram of a graphical user interface (GUI) forpresenting author profile information for a selected corpora andreceiving user selections of priorities for the author profiles inaccordance with one illustrative embodiment;

FIG. 5 is a flowchart outlining an example operation for correlating adocument profile with an author profile in accordance with oneillustrative embodiment; and

FIG. 6 is a flowchart outlining an example operation for prioritizedloading of documents from a corpus of information based on authorprofiles in accordance with one illustrative embodiment.

DETAILED DESCRIPTION

The illustrative embodiments provide mechanisms for performingauthorship enhanced ingestion of documents that are part of a corpus ofinformation for purposes of performing a natural language processingoperation on the document. Natural Language Processing (NLP) is atechnique that facilitates exchange of information between humans anddata processing systems. For example, one branch of NLP pertains totransforming a given content into a human-usable language or form. Forexample, NLP can accept a document whose content is in acomputer-specific language or form, and produce a document whosecorresponding content is in a human-readable form.

Modern NLP algorithms are based on machine learning, especiallystatistical machine learning. One example implementation of NLPmechanisms that utilize machine learning is a question and answer (QA)system, such as the Watson™ QA system previously mentioned above. Theparadigm of machine learning is different from that of most priorattempts at language processing which typically involved the direct handcoding of large sets of rules. The machine-learning paradigm callsinstead for using general learning algorithms, often, although notalways, grounded in statistical inference, to automatically learn suchrules through the analysis of large corpora of typical real-worldexamples. A corpus (plural, “corpora”) is a select set of documents,fragments (e.g., individual sentences, paragraphs, phrases, etc.), ordata instances from one or more data sources. A corpus may be annotatedwith the correct values to be learned via a machine learning mechanism.

Many different classes of machine learning algorithms have been appliedto NLP tasks. These algorithms take as input a large set of “features”that are generated from the input data. Some of the earliest-usedalgorithms, such as decision trees, produced systems of if-then rulesbased on these features, similar to the systems of hand-written rulesthat were then common. Increasingly, however, research has focused onstatistical models which make probabilistic decisions based on attachingreal-valued weights to each input feature. Such models have theadvantage that they can express the relative certainty of many differentpossible answers rather than only one, producing more reliable resultswhen such a model is included as a component of a larger system.

Often when working with massive sets of unstructured information, suchas the documents in a corpus of information being ingested by a NLPmechanism or QA system, for example, there are documents in the setwhich have missing information. This missing information may include,for example, authorship information, attribution information, relevantdates of the materials presented in the document, or the like. NLP andQA systems may need such missing information to assist the QA system,for example, in effectively managing the QA system operations, such asprioritizing the loading of documents into memory or storage forevaluation by the QA system, establishing networks of relationshipsbetween documents in the corpus of information, or other QA systemoperations.

The missing information in the documents may be missing for a variety ofdifferent reasons. For example, the original authorship information of adocument may have been lost over time, e.g., the author of the classicalwork the “Illiad” is not known, or the author wished to remainanonymous. As another example, data may have been lost in transmissionfrom its original source or may have been corrupted when a document wasconverted from a hard-copy form to a digital form, e.g., a malfunctionin the ingestion, a segment of a backup of a document is invalid,encrypted, or unrecoverable. As yet another example, an author may havechosen not to include the relevant authorship information because it wasnot deemed to be important based on the author's intended use of thedocument, e.g., a document of scientific notes may have not beenintended to be consumed by others but acts as a log for the author.

In normal data mining activities, the missing data is determined frompredetermined formulas and calculations or the data is discarded if itcannot be determined. However, this approach is not ideal for QA systemswhere more documents in the corpus that is ingested means that higherquality results are obtained, i.e. it is not beneficial in a QA systemto discard documents because the authorship information is missing.

With the mechanisms of the illustrative embodiments, a corpus ofinformation is selected for ingestion by a system, such as a QA system,NLP system or the like. Each document of the corpus of information isloaded by the system (e.g., QA system, NLP system, or the like) andanalyzed using natural language processing techniques to extract keyidentifiers, associative natural language components/elements, and thelike (referred to collectively as “features”) from the document tothereby generate a profile of the document based on these extractedfeatures. Among the extracted features there may be authorship featuresextracted from the document that provides an indication as to theidentity of the author and/or a classification of the authorship of thedocument that may correspond to one or more predefined author profiles.The profile may be for the entire document or a portion of the documentand is also referred to herein as a “content profile” meaning that theprofile is associated with a particular corresponding portion of contentof the document, e.g., the entire document or a sub-portion of thedocument.

The predefined author profiles may be specific to particular authors,e.g., an author profile may be associated with “Stephen King,” or may bea group classification identifying a group of one or more authors basedon characteristics of the authors that fall within the defined group.For example, a first author profile may be defined for “Subject MatterExperts” and may be defined in terms of the characteristics of authorsthat would be considered “Subject Matter Experts” including specificnames of individuals that have been recognized as subject matterexperts, publications in which documents associated with thepublications are considered to be authored by subject matter experts,organizations for which authors working for those organizations areconsidered to be subject matter experts, a threshold number ofreferences to a document being indicative of the document being authoredby a subject matter expert, a threshold number of documents directed toa same topic or domain being indicative of the corresponding authorbeing a subject matter expert, or any of a plethora of othercharacteristics of authors, documents, publications, organizations, orthe like, that are indicative of a category or classification of theauthorship of a document.

Similarly, other group classifications for defining author profiles maybe used. For example, another author profile may be defined for “PowerUsers” and a third author profile may be for “Novice” authors. Variousfeatures may be associated with such definitions of author profiles. Forexample, one feature may be that writings that use slang terms, “uh” or“um”, or other non-formal language may be considered indicative of a“Novice” author while technical terms, references to trade journals, andthe like, may be indicative of a “Subject Matter Expert.” Anycombination of features may be used to define different author profiles.

The author profiles may be defined for particular domains, topics,subject matter, or the like, with which the corpus of information isassociated. For example, a corpus of information may be defined for“medical” documentation and corresponding author profiles may beassociated with this corpus of information to thereby identify, forexample, subject matter experts, power users, and novices with regard tothe medical domain. The domain, topics, and subject matter definitionsmay be of various granularities, e.g., a domain for “medical”, anotherdomain for “cancer treatment”, a third domain for “Hodgkin's Lymphoma,”or the like. Thus, various author profiles may be defined for variouscorpora.

The definitions of the author profiles define the requirements for adocument to be associated with the author profile. When determiningwhether a document is associated with a particular author profile, theextracted features of the document in the document profile, or contentprofile, may be compared against the features and relationships betweenfeatures defined in the author profile to determine if the documentsatisfies the requirements for being associated with the author profile.In some illustrative embodiments, not all of the requirements of anauthor profile need to be met by the extracted features in the documentprofile. A predetermined threshold degree of matching of the extractedfeatures of the document profile with the author profile may be definedand as long as the document profile equals or exceeds this thresholddegree of matching, the document may be associated with the authorprofile.

It should be appreciated that the author profiles and the documentprofiles may not necessarily have a same configuration or organization.That is, in some illustrative embodiments, the author profiles anddocument profiles may have different fields from one another with only asubset of the fields of one profile being similar or related to one ormore of the fields of the other profile. Thus, for example, an authorprofile may have many different fields that are not in a documentprofile, but may include a field for names of publications associatedwith the author and an author name. Similarly, a document profile mayhave many different fields but may also include a publication name forthe document and an author name. The corresponding fields of theprofiles may be compared to determine degrees of matching between theauthor profiles and the document, or content, profiles.

In addition, a default author profile may be provided for documents thatdo not satisfy the requirements for association with any of the otherauthor profiles. Thus, for example, if the extracted features of adocument do not sufficiently match any of the author profiles, then thedocument may be automatically associated with the default authorprofile. The consideration of author profile matching may be performedwith regard to a single author of a document or a collection of multipleauthors of a document. Thus, in some illustrative embodiments, a singledocument may be associated with multiple author profiles if multipleauthors are associated with the document. In other illustrativeembodiments, the authorship as a whole of the document may be consideredfor determining a single author profile with which the document is to beassociated.

A data structure may be created and maintained in association with eachof the predefined author profiles. The data structure stores anidentifier, pointer, or other information about documents associatedwith the corresponding author profile such that the associated documentsmay be retrieved when ingesting documents for purposes of performing QAsystem or NLP system operations. Alternatively, the data structure maybe an evaluation criteria instead of a data structure storing pointersto the documents the evaluation criteria may be used to filter. In sucha case the data structure may be used to extract needed documentsmeeting the evaluation criteria from a larger corpus.

In one example embodiment, a user may specify, through a graphical userinterface or the like, a desired priority by which to ingest documentsfor QA system or NLP system operations. The priority of ingestion ofdocuments may be based on author profile. For example, a user mayspecify that they wish the QA system or NLP system to ingest and operateon “Subject Matter Expert” documents in a prioritized manner over otherdocuments of other author profiles. Similarly, a user may specify aseries of priorities for various author profiles such that a firstauthor profile may be considered with highest priority, a second authorprofile may be considered with second highest priority, and so forth.

The graphical user interface may present author profile information fora selected corpus of information in any of a number of different ways.For example, the graphical user interface may present a table of authorprofiles, some common characteristics defining the author profiles, orother information that specifically identifies the types of authors anddocuments associated with the author profile. In other embodiments, thegraphical user interface may present Venn diagrams or other graphicalrepresentations of the author profiles to assist a user is selecting aprioritization of author profiles for consideration by the system (QAsystem, NLP system, or the like).

The prioritization of the author profiles may be used to determinevarious ways in which to prioritize a subset of the corpus ofinformation based on authorship characteristics of the documents in thecorpus of information. Such prioritization may take the form ofidentifying which subset of documents in the corpus of information toingest and operate on first, second, third, and so on. Suchprioritization may affect the manner by which documents are scored whendetermining a degree of matching of the document with user submittedrequests, e.g., user submitted questions to a QA system, requests forretrieving documents based on a search, requests to perform other NLPoperations on documents, or the like. In some illustrative embodiments,the prioritization of author profiles may result in certain authorprofiles, and their associated subsets of documents from the corpus ofinformation, being removed or eliminated from consideration by thesystem. If a document exists in association with multiple authorprofiles, then a conflict resolution may be performed such that if thedocument exists in association with any of the author profiles that havebeen given higher priorities, then the document is loaded and consideredby the system, even if the document is also associated with a relativelylow priority author profile that would otherwise be eliminated orignored by the system.

The above aspects and advantages of the illustrative embodiments of thepresent invention will be described in greater detail hereafter withreference to the accompanying figures. It should be appreciated that thefigures are only intended to be illustrative of exemplary embodiments ofthe present invention. The present invention may encompass aspects,embodiments, and modifications to the depicted exemplary embodiments notexplicitly shown in the figures but would be readily apparent to thoseof ordinary skill in the art in view of the present description of theillustrative embodiments.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method, or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in any one or more computer readablemedium(s) having computer usable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be a system, apparatus, or device of an electronic,magnetic, optical, electromagnetic, or semiconductor nature, anysuitable combination of the foregoing, or equivalents thereof. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical device havinga storage capability, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiberbased device, a portable compact disc read-only memory (CDROM), anoptical storage device, a magnetic storage device, or any suitablecombination of the foregoing. In the context of this document, acomputer readable storage medium may be any tangible medium that cancontain or store a program for use by, or in connection with, aninstruction execution system, apparatus, or device.

In some illustrative embodiments, the computer readable medium is anon-transitory computer readable medium. A non-transitory computerreadable medium is any medium that is not a disembodied signal orpropagation wave, i.e. pure signal or propagation wave per se. Anon-transitory computer readable medium may utilize signals andpropagation waves, but is not the signal or propagation wave itself.Thus, for example, various forms of memory devices, and other types ofsystems, devices, or apparatus, that utilize signals in any way, suchas, for example, to maintain their state, may be considered to benon-transitory computer readable media within the scope of the presentdescription.

A computer readable signal medium, on the other hand, may include apropagated data signal with computer readable program code embodiedtherein, for example, in a baseband or as part of a carrier wave. Such apropagated signal may take any of a variety of forms, including, but notlimited to, electro-magnetic, optical, or any suitable combinationthereof. A computer readable signal medium may be any computer readablemedium that is not a computer readable storage medium and that cancommunicate, propagate, or transport a program for use by or inconnection with an instruction execution system, apparatus, or device.Similarly, a computer readable storage medium is any computer readablemedium that is not a computer readable signal medium.

Computer code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, radio frequency (RF), etc., or anysuitable combination thereof.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java™, Smalltalk™, C++, or the like, and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer, or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to the illustrativeembodiments of the invention. It will be understood that each block ofthe flowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions thatimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus, or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

Thus, the illustrative embodiments may be utilized in many differenttypes of data processing environments. The mechanisms of theillustrative embodiments allow for prioritization of the ingestion of acorpus of information based on associations of documents with authorprofiles and user selection of priorities of author profiles. Asmentioned above, the illustrative embodiments may be implemented withany analysis mechanism that operates on documents, portions of text, orother works of authorship that may be presented in an electronic form.In the illustrative embodiments, the analysis mechanism is considered tobe a computerized system that performs natural language processing (NLP)operations on electronic documents, portions of text, or the like. Inthe example embodiments described herein, it will be assumed that theNLP operations are part of a question and answer (QA) system, such asthe Watson™ QA system available from International Business MachinesCorporation of Armonk, N.Y. It should be appreciated that the Watson™ QAsystem, and QA systems in general, are only used as an example and theillustrative embodiments are not limited to such.

FIGS. 1-3 are directed to describing an example Question/Answer,Question and Answer, or Question Answering (QA) system, methodology, andcomputer program product with which the mechanisms of the illustrativeembodiments may be implemented. As will be discussed in greater detailhereafter, the illustrative embodiments may be integrated in, and mayaugment and extend the functionality of, these QA mechanisms with regardto prioritizing the portions of a corpus of information ingested by theQA mechanisms based on associations of the portions of the corpus ofinformation with author profiles. For purposes of the followingdescription, the portions of the corpus of information will beconsidered to be one or more documents of the corpus of information. Itshould be appreciated, however, that a corpus of information maycomprise statements, sub-portions of documents, or any other amount oftextual content that may or may not be considered a “document.” Theillustrative embodiments may be applicable to any portion of textwhether it be part of an electronic document or not.

Using a QA system as an example of the type of system in which theillustrative embodiments may be implemented, it is important to firsthave an understanding of how question and answer creation in a QA systemmay be implemented before describing how the mechanisms of theillustrative embodiments are integrated in and augment such QA systems.It should be appreciated that the QA mechanisms described in FIGS. 1-3are only examples and are not intended to state or imply any limitationwith regard to the type of QA mechanisms with which the illustrativeembodiments may be implemented. Many modifications to the example QAsystem shown in FIGS. 1-3 may be implemented in various embodiments ofthe present invention without departing from the spirit and scope of thepresent invention.

QA mechanisms operate by accessing information from a corpus of data orinformation (also referred to as a corpus of content), analyzing it, andthen generating answer results based on the analysis of this data.Accessing information from a corpus of data typically includes: adatabase query that answers questions about what is in a collection ofstructured records, and a search that delivers a collection of documentlinks in response to a query against a collection of unstructured data(text, markup language, etc.). Conventional question answering systemsare capable of generating answers based on the corpus of data and theinput question, verifying answers to a collection of questions for thecorpus of data, correcting errors in digital text using a corpus ofdata, and selecting answers to questions from a pool of potentialanswers, i.e. candidate answers.

Content creators, such as article authors, electronic document creators,web page authors, document database creators, and the like, maydetermine use cases for products, solutions, and services described insuch content before writing their content. Consequently, the contentcreators may know what questions the content is intended to answer in aparticular topic addressed by the content. Categorizing the questions,such as in terms of roles, type of information, tasks, or the like,associated with the question, in each document of a corpus of data mayallow the QA system to more quickly and efficiently identify documentscontaining content related to a specific query. The content may alsoanswer other questions that the content creator did not contemplate thatmay be useful to content users. The questions and answers may beverified by the content creator to be contained in the content for agiven document. These capabilities contribute to improved accuracy,system performance, machine learning, and confidence of the QA system.Content creators, automated tools, or the like, may annotate orotherwise generate metadata for providing information useable by the QAsystem to identify these question and answer attributes of the content.

Operating on such content, the QA system generates answers for inputquestions using a plurality of intensive analysis mechanisms whichevaluate the content to identify the most probable answers, i.e.candidate answers, for the input question. The illustrative embodimentsleverage the work already done by the QA system to reduce thecomputation time and resource cost for subsequent processing ofquestions that are similar to questions already processed by the QAsystem.

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aquestion/answer creation (QA) system 100 in a computer network 102. Oneexample of a question/answer generation which may be used in conjunctionwith the principles described herein is described in U.S. PatentApplication Publication No. 2011/0125734, which is herein incorporatedby reference in its entirety. The QA system 100 may be implemented onone or more computing devices 104 (comprising one or more processors andone or more memories, and potentially any other computing deviceelements generally known in the art including buses, storage devices,communication interfaces, and the like) connected to the computernetwork 102. The network 102 may include multiple computing devices 104in communication with each other and with other devices or componentsvia one or more wired and/or wireless data communication links, whereeach communication link may comprise one or more of wires, routers,switches, transmitters, receivers, or the like. The QA system 100 andnetwork 102 may enable question/answer (QA) generation functionality forone or more QA system users via their respective computing devices110-112. Other embodiments of the QA system 100 may be used withcomponents, systems, sub-systems, and/or devices other than those thatare depicted herein.

The QA system 100 may be configured to implement a QA system pipeline108 that receive inputs from various sources. For example, the QA system100 may receive input from the network 102, a corpus of electronicdocuments 106, QA system users, or other data and other possible sourcesof input. In one embodiment, some or all of the inputs to the QA system100 may be routed through the network 102. The various computing devices104 on the network 102 may include access points for content creatorsand QA system users. Some of the computing devices 104 may includedevices for a database storing the corpus of data 106 (which is shown asa separate entity in FIG. 1 for illustrative purposes only). Portions ofthe corpus of data 106 may also be provided on one or more other networkattached storage devices, in one or more databases, or other computingdevices not explicitly shown in FIG. 1. The network 102 may includelocal network connections and remote connections in various embodiments,such that the QA system 100 may operate in environments of any size,including local and global, e.g., the Internet.

In one embodiment, the content creator creates content in a document ofthe corpus of data 106 for use as part of a corpus of data with the QAsystem 100. The document may include any file, text, article, or sourceof data for use in the QA system 100. QA system users may access the QAsystem 100 via a network connection or an Internet connection to thenetwork 102, and may input questions to the QA system 100 that may beanswered by the content in the corpus of data 106. In one embodiment,the questions may be formed using natural language. The QA system 100may interpret the question and provide a response to the QA system user,e.g., QA system user 110, containing one or more answers to thequestion. In some embodiments, the QA system 100 may provide a responseto users in a ranked list of candidate answers.

The QA system 100 implements a QA system pipeline 108 which comprises aplurality of stages for processing an input question, the corpus of data106, and generating answers for the input question based on theprocessing of the corpus of data 106. The QA system pipeline 108 will bedescribed in greater detail hereafter with regard to FIG. 3.

In some illustrative embodiments, the QA system 100 may be the Watson™QA system available from International Business Machines Corporation ofArmonk, N.Y., which is augmented with the mechanisms of the illustrativeembodiments described hereafter. The Watson™ QA system may receive aninput question which it then parses to extract the major features of thequestion, that in turn are then used to formulate queries that areapplied to the corpus of data. Based on the application of the queriesto the corpus of data, a set of hypotheses, or candidate answers to theinput question, are generated by looking across the corpus of data forportions of the corpus of data that have some potential for containing avaluable response to the input question.

The Watson™ QA system then performs deep analysis on the language of theinput question and the language used in each of the portions of thecorpus of data found during the application of the queries using avariety of reasoning algorithms. There may be hundreds or even thousandsof reasoning algorithms applied, each of which performs differentanalysis, e.g., comparisons, and generates a score. For example, somereasoning algorithms may look at the matching of terms and synonymswithin the language of the input question and the found portions of thecorpus of data. Other reasoning algorithms may look at temporal orspatial features in the language, while others may evaluate the sourceof the portion of the corpus of data and evaluate its veracity.

The scores obtained from the various reasoning algorithms indicate theextent to which the potential response is inferred by the input questionbased on the specific area of focus of that reasoning algorithm. Eachresulting score is then weighted against a statistical model. Thestatistical model captures how well the reasoning algorithm performed atestablishing the inference between two similar passages for a particulardomain during the training period of the Watson™ QA system. Thestatistical model may then be used to summarize a level of confidencethat the Watson™ QA system has regarding the evidence that the potentialresponse, i.e. candidate answer, is inferred by the question. Thisprocess may be repeated for each of the candidate answers until theWatson™ QA system identifies candidate answers that surface as beingsignificantly stronger than others and thus, generates a final answer,or ranked set of answers, for the input question. More information aboutthe Watson™ QA system may be obtained, for example, from the IBMCorporation website, IBM Redbooks, and the like. For example,information about the Watson™ QA system can be found in Yuan et al.,“Watson and Healthcare,” IBM developerWorks, 2011 and “The Era ofCognitive Systems: An Inside Look at IBM Watson and How it Works” by RobHigh, IBM Redbooks, 2012.

FIG. 2 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments may be implemented. Dataprocessing system 200 is an example of a computer, such as server 104 orclient 110 in FIG. 1, in which computer usable code or instructionsimplementing the processes for illustrative embodiments of the presentinvention may be located. In one illustrative embodiment, FIG. 2represents a server computing device, such as a server 104, which, whichimplements a QA system 100 and QA system pipeline 108 augmented toinclude the additional mechanisms of the illustrative embodimentsdescribed hereafter.

In the depicted example, data processing system 200 employs a hubarchitecture including north bridge and memory controller hub (NB/MCH)202 and south bridge and input/output (I/O) controller hub (SB/ICH) 204.Processing unit 206, main memory 208, and graphics processor 210 areconnected to NB/MCH 202. Graphics processor 210 may be connected toNB/MCH 202 through an accelerated graphics port (AGP).

In the depicted example, local area network (LAN) adapter 212 connectsto SB/ICH 204. Audio adapter 216, keyboard and mouse adapter 220, modem222, read only memory (ROM) 224, hard disk drive (HDD) 226, CD-ROM drive230, universal serial bus (USB) ports and other communication ports 232,and PCI/PCIe devices 234 connect to SB/ICH 204 through bus 238 and bus240. PCI/PCIe devices may include, for example, Ethernet adapters,add-in cards, and PC cards for notebook computers. PCI uses a card buscontroller, while PCIe does not. ROM 224 may be, for example, a flashbasic input/output system (BIOS).

HDD 226 and CD-ROM drive 230 connect to SB/ICH 204 through bus 240. HDD226 and CD-ROM drive 230 may use, for example, an integrated driveelectronics (IDE) or serial advanced technology attachment (SATA)interface. Super I/O (SIO) device 236 may be connected to SB/ICH 204.

An operating system runs on processing unit 206. The operating systemcoordinates and provides control of various components within the dataprocessing system 200 in FIG. 2. As a client, the operating system maybe a commercially available operating system such as Microsoft® Windows7°. An object-oriented programming system, such as the Java™ programmingsystem, may run in conjunction with the operating system and providescalls to the operating system from Java™ programs or applicationsexecuting on data processing system 200.

As a server, data processing system 200 may be, for example, an IBM®eServer™ System p® computer system, running the Advanced InteractiveExecutive (AIX®) operating system or the LINUX® operating system. Dataprocessing system 200 may be a symmetric multiprocessor (SMP) systemincluding a plurality of processors in processing unit 206.Alternatively, a single processor system may be employed.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs are located on storage devices,such as HDD 226, and may be loaded into main memory 208 for execution byprocessing unit 206. The processes for illustrative embodiments of thepresent invention may be performed by processing unit 206 using computerusable program code, which may be located in a memory such as, forexample, main memory 208, ROM 224, or in one or more peripheral devices226 and 230, for example.

A bus system, such as bus 238 or bus 240 as shown in FIG. 2, may becomprised of one or more buses. Of course, the bus system may beimplemented using any type of communication fabric or architecture thatprovides for a transfer of data between different components or devicesattached to the fabric or architecture. A communication unit, such asmodem 222 or network adapter 212 of FIG. 2, may include one or moredevices used to transmit and receive data. A memory may be, for example,main memory 208, ROM 224, or a cache such as found in NB/MCH 202 in FIG.2.

Those of ordinary skill in the art will appreciate that the hardwaredepicted in FIGS. 1 and 2 may vary depending on the implementation.Other internal hardware or peripheral devices, such as flash memory,equivalent non-volatile memory, or optical disk drives and the like, maybe used in addition to or in place of the hardware depicted in FIGS. 1and 2. Also, the processes of the illustrative embodiments may beapplied to a multiprocessor data processing system, other than the SMPsystem mentioned previously, without departing from the spirit and scopeof the present invention.

Moreover, the data processing system 200 may take the form of any of anumber of different data processing systems including client computingdevices, server computing devices, a tablet computer, laptop computer,telephone or other communication device, a personal digital assistant(PDA), or the like. In some illustrative examples, data processingsystem 200 may be a portable computing device that is configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data, for example. Essentially, dataprocessing system 200 may be any known or later developed dataprocessing system without architectural limitation.

FIG. 3 illustrates a QA system pipeline for processing an input questionin accordance with one illustrative embodiment. The QA system pipelineof FIG. 3 may be implemented, for example, as QA system pipeline 108 ofQA system 100 in FIG. 1. It should be appreciated that the stages of theQA system pipeline shown in FIG. 3 may be implemented as one or moresoftware engines, components, or the like, which are configured withlogic for implementing the functionality attributed to the particularstage. Each stage may be implemented using one or more of such softwareengines, components or the like. The software engines, components, etc.may be executed on one or more processors of one or more data processingsystems or devices and may utilize or operate on data stored in one ormore data storage devices, memories, or the like, on one or more of thedata processing systems. The QA system pipeline of FIG. 3 may beaugmented, for example, in one or more of the stages to implement theimproved mechanism of the illustrative embodiments described hereafter,additional stages may be provided to implement the improved mechanism,or separate logic from the pipeline 300 may be provided for interfacingwith the pipeline 300 and implementing the improved functionality andoperations of the illustrative embodiments

As shown in FIG. 3, the QA system pipeline 300 comprises a plurality ofstages 310-380 through which the QA system operates to analyze an inputquestion and generate a final response. In an initial question inputstage 310, the QA system receives an input question that is presented ina natural language format. That is, a user may input, via a userinterface, an input question for which the user wishes to obtain ananswer, e.g., “Who are Washington's closest advisors?” In response toreceiving the input question, the next stage of the QA system pipeline300, i.e. the question and topic analysis stage 320, parses the inputquestion using natural language processing (NLP) techniques to extractmajor features from the input question, classify the major featuresaccording to types, e.g., names, dates, or any of a plethora of otherdefined topics. For example, in the example question above, the term“who” may be associated with a topic for “persons” indicating that theidentity of a person is being sought, “Washington” may be identified asa proper name of a person with which the question is associated,“closest” may be identified as a word indicative of proximity orrelationship, and “advisors” may be indicative of a noun or otherlanguage topic.

The identified major features may then be used during the questiondecomposition stage 330 to decompose the question into one or morequeries that may be applied to the corpus of data/information 345 inorder to generate one or more hypotheses. The queries may be generatedin any known or later developed query language, such as the StructureQuery Language (SQL), or the like. The queries may be applied to one ormore databases storing information about the electronic texts,documents, articles, websites, and the like, that make up the corpus ofdata/information 345. That is, these various sources themselves,collections of sources, and the like, may represent different corpus 347within the corpora 345. There may be a different corpus 347 defined fordifferent collections of documents based on various criteria dependingupon the particular implementation. For example, different corpora maybe established for different topics, subject matter categories, sourcesof information, or the like. As one example, a first corpus may beassociated with healthcare documents while a second corpus may beassociated with financial documents. Alternatively, one corpus may bedocuments published by the U.S. Department of Energy while anothercorpus may be IBM Redbooks documents. Any collection of content havingsome similar attribute may be considered to be a corpus 347 within thecorpora 345.

The queries may be applied to one or more databases storing informationabout the electronic texts, documents, articles, websites, and the like,that make up the corpus of data/information, e.g., the corpus of data106 in FIG. 1. The queries being applied to the corpus ofdata/information at the hypothesis generation stage 340 to generateresults identifying potential hypotheses for answering the inputquestion which can be evaluated. That is, the application of the queriesresults in the extraction of portions of the corpus of data/informationmatching the criteria of the particular query. These portions of thecorpus may then be analyzed and used, during the hypothesis generationstage 340, to generate hypotheses for answering the input question.These hypotheses are also referred to herein as “candidate answers” forthe input question. For any input question, at this stage 340, there maybe hundreds of hypotheses or candidate answers generated that may needto be evaluated.

The QA system pipeline 300, in stage 350, then performs a deep analysisand comparison of the language of the input question and the language ofeach hypothesis or “candidate answer” as well as performs evidencescoring to evaluate the likelihood that the particular hypothesis is acorrect answer for the input question. As mentioned above, this mayinvolve using a plurality of reasoning algorithms, each performing aseparate type of analysis of the language of the input question and/orcontent of the corpus that provides evidence in support of, or not, ofthe hypothesis. Each reasoning algorithm generates a score based on theanalysis it performs which indicates a measure of relevance of theindividual portions of the corpus of data/information extracted byapplication of the queries as well as a measure of the correctness ofthe corresponding hypothesis, i.e. a measure of confidence in thehypothesis.

In the synthesis stage 360, the large number of relevance scoresgenerated by the various reasoning algorithms may be synthesized intoconfidence scores for the various hypotheses. This process may involveapplying weights to the various scores, where the weights have beendetermined through training of the statistical model employed by the QAsystem and/or dynamically updated, as described hereafter. The weightedscores may be processed in accordance with a statistical model generatedthrough training of the QA system that identifies a manner by whichthese scores may be combined to generate a confidence score or measurefor the individual hypotheses or candidate answers. This confidencescore or measure summarizes the level of confidence that the QA systemhas about the evidence that the candidate answer is inferred by theinput question, i.e. that the candidate answer is the correct answer forthe input question.

The resulting confidence scores or measures are processed by a finalconfidence merging and ranking stage 370 which may compare theconfidence scores and measures, compare them against predeterminedthresholds, or perform any other analysis on the confidence scores todetermine which hypotheses/candidate answers are the most likely to bethe answer to the input question. The hypotheses/candidate answers maybe ranked according to these comparisons to generate a ranked listing ofhypotheses/candidate answers (hereafter simply referred to as “candidateanswers”). From the ranked listing of candidate answers, at stage 380, afinal answer and confidence score, or final set of candidate answers andconfidence scores, may be generated and output to the submitter of theoriginal input question.

As shown in FIG. 3, in accordance the illustrative embodiments, a corpusselection and author prioritization engine 390 is provided along with andocument/author profile correlation engine 395 for permitting a user toselect what corpus of information 347, from the corpora 345, to use as abasis for generating candidate answers to an input question 310 andselecting a priority of author profiles to utilize when ingesting theselected corpus 347. The document/author profile correlation engine 395may operate periodically, in response to an event, or continuously toanalyze documents of various corpora 345 and correlate the documentswith one or more predefined author profiles of a plurality of predefinedauthor profiles 397, as described further hereafter. The corpusselection and author prioritization engine 390 provides user interfacesthrough which a user may select a corpus 347 and further may bepresented with corresponding author profiles from the author profiledata structure 397, which the user may select and/or prioritize tothereby control the ingestion of the documents from the selected corpus347, as also described hereafter.

The author profiles data structure 397 stores author profiles that aredefined for the various corpora 345 upon which the QA system pipeline300 may operate. There author profiles data structure 397 may storeseparate sets of author profiles for each corpus in the corpora 345 suchthat one set of author profiles may be different from another. Thedefinition of these author profiles may be performed in a manual manner,automatic manner, or semi-automatic manner. For example, a systemadministrator may be presented with a graphical user interface (GUI),for example via the corpus selection and author prioritization engine390, through which a human system administrator may set forth features,relationships between features, thresholds, and rules for defining anauthor profile. These features may be directed to authorship informationfor documents, publication information for documents, source informationfor documents, numbers of related documents, numbers of references tothe document, number of references in the document to other documents,numbers of documents authored by the same author in the related corpus,or any of a plethora of other possible features and relationships,thresholds, or rules associated with such features. The resulting authorprofiles generated by the system administrator may be stored inassociation with the corresponding corpus 347 in the author profilesdata structure 397. There may be multiple author profiles generated andstored for a single corpus 347 and an author profile may also be sharedacross a plurality of corpora 345 by being associated with the variouscorpora 345 in the author profiles data structure 397.

In some embodiments, the corpus selection and author prioritizationengine 390 may implement automated mechanisms for generating authorprofiles based on analysis of documents in a corpus 347. For example,through analysis of a document, various features related to theauthorship, publication, source, etc., may be extracted. Similarities ofthese extracted features with extracted features of other documents inthe same corpus 347 may be identified through these automatedmechanisms, these similar features being directed to identifying aprofile of the authors of the documents. For example, multiple documentsmay be published by the same publisher, have similar sources, and havethe same author, author credentials, or the like, and the author may beconsidered to be a “subject matter expert” with regard to the subjectmatter, or domain, of the particular corpus 347. As a result, theautomated mechanism may automatically generate an author profile of“subject matter expert” if one does not already exist, or mayautomatically update an existing “subject matter expert” profile, withthe similar features and/or relationships between similar features foundthrough the automated analysis. Of course a semi-automated process mayalso be used in which automated mechanisms provide suggestions as to newauthor profiles or updates to existing author profiles and presentsthese to a human system administrator for approval or denial of thesuggested addition or update to the author profiles in the authorprofiles data structure 397.

For example, during training using a test corpus of information, thedocuments of the test corpus may be processed to generate variousprofiles including a “Nature” profile that stores phrases, keywords,patterns of terms, or other features extracted from documents pertainingto “Nature Expert.” Authors that may be considered “Nature Experts” willtend to use similar features to those extracted from the documents ofthe test corpus and associated with the “Nature Experts” author profile.For example, the terms “mushroom”, “fauna”, and “dendrophilia” may beassociated with the author profile “Nature Experts.”

In addition to defining author profiles for the various corpora 345 andassociating the author profiles with their corresponding corpora 345 inthe author profiles data structure 397, the mechanisms of theillustrative embodiments further correlate the documents within each ofthe corpora 345 with one or more author profiles associated with thecorresponding corpus. That is, the annotators used to perform analysisof documents for question answering as described previously, may also beused by the document/author profile correlation engine 395 to correlatedocuments within a corpus with one or more author profiles associatedwith that corpus 347. The document/author profile correlation engine 395may interface with the hypothesis generation stage 340 logic to make useof the annotators and the results of their processing on the documentsof a selected corpus 347 or may have a separate set of annotators orseparate interface with the annotators directly to achieve the analysisneeded to correlate a document profile with an author profile inaccordance with the illustrative embodiments.

The annotators extract features and natural language components from thedocuments including features directed to authorship, publication,source, etc. of the document. However, for performance purposes, ratherthan extracting all of the features and components from the document, asmall segment may be extracted, e.g., the header, first few pages, firstfew paragraphs, or the like. These extracted features may be used togenerate a document profile or model of the document. For example, adocument from an online corpus may be processed and features extractedto generate a document profile that includes the features “blackbird”,“redwinged blackbird”, and “ornithological”. Thus, from these featuresit can be determined that this document is a document associate withornithology and more specifically contains content directed toblackbirds, and in particular redwinged blackbirds.

The features maintained in a document profile of a document may becompared, by the document/author profile correlation engine 395, againstauthor profiles for the particular corpus 347, as retrieved from theauthor profiles data structure 397 via the corpus selection and authorprioritization engine 390. The comparison of the document profile to theauthor profiles for the corpus 347 is to determine a degree of matchingof the document profile with the requirements of the features,relationships between features, etc., set forth in the author profile.The degree of matching is a quantitative value that is a measure of howmuch of the requirements in the author profile are met or exceeded bythe contents of the document profile. In addition, information fromother document profiles may also be used to assist in this degree ofmatching evaluation such as to identify a number of documents in acorpus 347 that are authored by the same author, published by the samepublisher, have a same source, etc.

Using the previous two examples, the document profile for the blackbirddocument may be compared to the “Nature Expert” author profile. Sincethere is little similarity between these two profiles, the degree ofmatching for these profiles would be relatively low. As a result, if“Nature Expert” author profile related documents are prioritized forprocessing, then the blackbird document would not be selected as part ofthe set of prioritized documents associated with the “Nature Expert”author profile.

A degree of matching value may be generated for each of the authorprofiles associated with the particular corpus 347 in which the documentis present. That is, when the document/author profile correlation engine395 determines it is time to analyze the documents in a corpus 347, suchas by a specific request to do so from a system administrator via thecorpus selection and author prioritization engine 390, a periodic timeinterval, an event occurring such as a change or update being made to acorpus 347 to add/remove/modify a document in the corpus 347, or thelike, the documents of a selected corpus 347 are analyzed to generatedocument profiles for the documents and these document profiles arecompared against each of the predefined author profiles for the selectedcorpus 347 to thereby generate a degree of matching value for thedocument profile for each of the author profiles. These degree ofmatching values are then compared by the document/author profilecorrelation engine 395 to one or more threshold values maintained by thedocument/author profile correlation engine 395 to determine which degreeof matching values equal or exceed these one or more threshold valuesand thereby indicate a correlation between the document profile and thecorresponding author profile. The one or more threshold values may bepre-defined by a system administrator or other authorized person and maybe modified to adjust the operation of the document/author profilecorrelation engine 395 based on changing conditions of the QA systempipeline 300 or to achieve a desired operation of the QA system pipeline300.

Assuming that at least one of the document profile's degree of matchingvalues for the various author profiles equals or exceeds the one or morethreshold values, an identifier, link, address, or other identifier ofthe corresponding document is added to a correlation entry in thecorrelation data structure 399 in association with the author profileand corpus identifier. Thus, entries in the correlation data structure399 comprise a corpus identifier, an author profile identifier, and zeroor more document identifiers to thereby correlate the document with amatching author profile of a particular corpus. This process may berepeated for each document in the selected corpus 347 and may further berepeated across each document of each corpus 347 in the corpora 345. Ifthe operation of the document/author profile correlation engine 395 istriggered because of a change to a particular corpus 347, the operationsdescribed above may be performed specifically for only the changeddocuments in the corpus 347, e.g., if a new document is added to thecorpus 347, then the operations may be performed with regard to only thenew document while information obtained for the other documents of thecorpus 347 may be unchanged and may be used to assist in the operationsbeing performed for the new document, e.g., number of documents authoredby the same author, etc.

Thus, through the operation of the document/author profile correlationengine 395, a correspondence between document profiles and authorprofiles is achieved and maintained in the correlation data structure399. This correlation is based on the analysis of documents performed byannotators to extract features from the documents and logic of thedocument/author profile correlation engine 395 to generate a documentprofile based on these extracted features. Moreover, this correlation isbased on the establishment of author profiles for a selected corpuseither in a manual, automated, or semi-automated manner via the corpusselection and author prioritization engine 390. The resultingcorrelation data structure 399 entries may be used when handling usersubmitted requests for natural language processing operations, such asanswering a submitted question via the QA system pipeline 300 in thedepicted example, as described hereafter.

With the mechanisms of the illustrative embodiment depicted in FIG. 3, auser may input an input question 310 that the user wishes to be answeredby the QA system pipeline 300 based on information in the corpora 345.The input question stage 310 logic may present a GUI through which auser may submit the input question 310. This GUI, in accordance with theillustrative embodiments, may further provide options for the user toselect a corpus of information 347 which the user wishes to use as abasis for generating an answer to the input question 310. Based on auser's selection of a particular corpus 347, the user's selection may becommunicated to the corpus selection and author prioritization engine390 which then retrieves the associated author profiles for the selectedcorpus 347 from the author profiles data structure 397. A GUI indicatingthe available author profiles for the selected corpus 347 may bereturned to the user so that the user may select a priority by which toingest the documents of the selected corpus 347.

The GUI indicating the available author profiles for the selected corpus347 may be presented in any of a number of ways including in a tabularform, as a Venn diagram, or the like. The particular author profiles maybe presented by presenting a name of the author profile, e.g., “SubjectMatter Experts,” “Power Users,” or “Novice Authors,” or the like. Inaddition, features characteristic of the author profile may be listed toassist the user in selecting an appropriate priority of the authorprofiles, e.g., “Subject Matter Experts” may have the features ofrecognized expert authors in the particular subject matter, published injournals or trade periodicals, having a particular number of documentsauthored by the authors in the particular subject matter, etc.

The GUI indicating the available author profiles may further compriseuser selectable GUI elements, fields, or the like, through which theuser may specify a relative priority of the particular author profilerelative to the other author profiles available for the selected corpus347. The user may enter information into the GUI to specify which authorprofiles are considered more important to answer the input question 310than others to thereby customize the operation of the QA system pipeline300 to the user's desires regarding relative importance of authorship.The user input may be, for example, numerical entries specifying apriority ordering of the author profiles, selection of relativequalitative value indicators such as “highest”, “lowest” or the like,selection of a user interface element to remove documents associatedwith a particular author profile from further consideration by the QAsystem pipeline 300, or the like.

Based on the user's input specifying relative priorities of authorprofiles for the selected corpus 347, the QA system pipeline 300operations are tailored to the particular user input. For example, thismay involve loading only the documents associated with a highestpriority author profile, as determined from a corresponding entry forthe corpus 347 and the author profile obtained from the correlation datastructure 399, and evaluating the input question 310 against thosedocuments to determine if a sufficiently high scoring candidate answeris generated. If so, then further evaluation of documents associatedwith other author profiles of the selected corpus 347 need not beperformed. If not, then the documents associated with the next highestpriority author profile may be loaded and added to the documents alreadyconsidered and the QA system pipeline 300 operation applied to thisexpanded collection of loaded documents. This process may be repeatediteratively until a sufficiently high scoring candidate answer isidentified or all of the documents associated with author profilesselected for consideration by the user have been loaded and processedthrough the QA system pipeline 300.

In another illustrative embodiment, the user input specifying relativepriorities of author profiles may be used to adjust weights applied todocument scores generated by the hypothesis and evidence scoring stage350 logic. That is, the relative priorities may be used to more heavilyweight documents associated with the higher priority author profiles andless heavily weight documents associated with lower priority authorprofiles. In this way, the evidence and sources of candidate answersoriginating with documents of a higher priority author profile are givengreater credence and confidence than documents of lower priority authorprofiles. The weights may be quantitative values that are included inthe scoring functions applied by the hypothesis and evidence scoringstage 350 logic as variables that are populated based on the particularauthor profile with which the document is associated and the user'sinput as to a relative priority of the author profile. If a document isassociated with multiple author profiles, a highest priority rating ofthe author profiles with which the document is associated may be used.

Thus, through the mechanisms of the illustrative embodiments, a user isgiven the ability to customize or tailor the operation of the QA systempipeline 300, or other natural language processing system operation, tothe user's own subjective determination as to what author profiles aremore important or likely to generate correct results to a user request,e.g., a correct answer to an input question. The customized or tailoredoperation may be performed based on the author profiles associated witha user selected corpus 347 and the correlation of document profiles ofdocuments from the selected corpus 347 with the author profiles.

As mentioned above, the corpus selection and author prioritizationengine 390 may interface with the input question stage 310 logic, orotherwise provide one or more GUIs for use by a user to select a corpusof information 347 and specify priority ranking of author profiles forthe selected corpus of information 347. FIG. 4 is an example diagram ofa graphical user interface (GUI) for presenting author profileinformation for a selected corpora and receiving user selections ofpriorities for the author profiles in accordance with one illustrativeembodiment.

As shown in FIG. 4, the GUI 400 includes a first portion 410 throughwhich a user may select a corpus from a plurality of available corporaupon which the system operates. In the depicted example, the user hasselected the corpus corresponding to cancer treatments. While FIG. 4illustrates only one corpus being selected by the user, it should beappreciated that the user may select more than one corpora and themechanisms of the illustrative embodiments would be applied to each ofthe corpora selected by the user.

A second portion 420 of the GUI may be automatically populated inresponse to the user's selection of one or more of the available corporain the first portion 410 based on retrieved information for the authorprofiles associated with the selected corpus/corpora. GUI elements 430are provided for entry of priority information, such as a relativepriority ranking of the corresponding author profile. In the depictedexample, these take the form of a free form text field into which theuser may input a numerical ranking of the relative priorities, e.g., 1being a highest priority and 10 being a lowest priority. In otherillustrative embodiments, the GUI elements 430 may take the form ofdrop-down menus, radial buttons, check mark boxes, or any of a plethoraof known GUI elements. As shown in FIG. 4, additional GUI elements 440may be provided for a user to select to eliminate an author profile fromfurther consideration.

Based on the user input to the GUI 400, the system is informed of whichcorpora the user wishes to use for performing the system's operations,e.g., QA system pipeline operations or other natural language processingoperations, and which author profiles for the selected corpora areconsidered more/less important in generating the system operationresults. The user input is used by the system to tailor its operation tothe prioritized author profiles as previously described, therebyproviding customized operation for the user.

FIG. 5 is a flowchart outlining an example operation for correlating adocument profile with an author profile in accordance with oneillustrative embodiment. As shown in FIG. 5, the operation starts withidentifying a corpus to be analyzed (step 510) and then loading adocument from the corpus for analysis (step 520). The document isanalyzed to extract features from the document (step 530) and correlateextracted features with features of other documents in the corpus, e.g.,to identify numbers of documents authored by the same author in thecorpus or the like, if necessary (step 540). The operation generates adocument profile based on the extracted features and correlation ofextracted features with features of other documents in the corpus (step550).

Author profiles associated with the identified corpus are retrieved(step 560) and the document profile is compared to the author profile todetermine a degree of matching (step 570). The degree of matching iscompared to one or more threshold values (step 580) to determine if thedegree of matching equals or exceeds the one or more thresholds, i.e. todetermine if the one or more thresholds are met (step 590). If the oneor more thresholds are met, then the document associated with thedocument profile is associated with the author profile and corpus in acorrelation data structure entry (step 595). If the one or morethresholds are not met, then the document is not associated with theauthor profile. It should be appreciated that this operation may beperformed with regard to each author profile associated with theidentified corpus. Moreover, if the document profile cannot beassociated with any of the author profiles because the correspondingdegrees of matching do not meet the requirements of the one or morethresholds, the document may be associated with a default authorprofile.

A determination is made as to whether additional documents need to beprocessed in the corpus (step 597). If so, the operation returns to step520; otherwise the operation terminates.

FIG. 6 is a flowchart outlining an example operation for prioritizedloading of documents from a corpus of information based on authorprofiles in accordance with one illustrative embodiment. As shown inFIG. 6, the operation starts by presenting a GUI to a user for selectionof one or more corpora upon which to operate (step 610). Based on userinput to the GUI selecting the one or more corpora, author profilescorresponding to the selected corpora are retrieved (step 620) and theGUI is updated, or another GUI is generated, to include the authorprofiles corresponding to the selected corpora and providing GUIelements for specifying priorities for the various author profiles (step630). User input is received into the GUI to specify the priorities ofthe various author profiles (step 640) and an operation of the system ismodified based on the assigned priorities of the various author profilesso as to emphasize or give preferential treatment to documentsassociated with author profiles having relatively higher priorities(step 650). The operation then terminates.

Thus, the illustrative embodiments provide mechanisms for utilizingauthor profiles to prioritize the ingestion and natural languageprocessing of documents in one or more corpora of information. Theillustrative embodiments permit a user to specify how to customize theoperation of a natural language processing system to tailor theoperation to the user's own subjective determination as to theimportance of subsets of documents within one or more corpora. In thisway, document ingestion is prioritized based on user input.

As noted above, it should be appreciated that the illustrativeembodiments may take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment containing both hardwareand software elements. In one example embodiment, the mechanisms of theillustrative embodiments are implemented in software or program code,which includes but is not limited to firmware, resident software,microcode, etc.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers. Network adapters mayalso be coupled to the system to enable the data processing system tobecome coupled to other data processing systems or remote printers orstorage devices through intervening private or public networks. Modems,cable modems and Ethernet cards are just a few of the currentlyavailable types of network adapters.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the art. Theembodiment was chosen and described in order to best explain theprinciples of the invention, the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

What is claimed is:
 1. A method, in a data processing system comprisinga processor and a memory, for processing a corpus of information in anatural language processing system, the method comprising: receiving, bythe data processing system, a user input specifying at least one authorprofile indicating authors whose content is to be ingested by a naturallanguage processing system to perform natural language processing (NLP)operations based on ingested content; generating, by the data processingsystem, a content profile for a portion of content of a corpus ofinformation; comparing, by the data processing system, the contentprofile to the at least one author profile at least by, for each authorprofile in the at least one author profile: comparing each of one ormore first features extracted from the content profile to correspondingones of one or more second features extracted from the author profile;calculating a degree of matching between the each of the one or morefirst features and corresponding ones of the one or more second featuresto determine a level of matching of the content profile with the authorprofile; and comparing the level of matching with a predeterminedthreshold value; and controlling an ingestion operation of the naturalNLP system, for ingesting content from the corpus of information into amemory data structure for processing by the NLP system, based on resultsof comparing the level of matching with the predetermined threshold,wherein in response to results of the comparing of the level of matchingwith the predetermined threshold value indicating that the level ofmatching has a first relationship relative to the predeterminedthreshold, controlling the ingestion operation comprises not ingesting,by the NLP system, the portion of content, and wherein in response toresults of the comparing of the level of matching with the predeterminedthreshold value indicating that the level of matching has a secondrelationship relative to the predetermined threshold, controlling theingestion operation comprises ingesting the portion of content by theNLP system.
 2. The method of claim 1, wherein the comparing of thecontent profile to the at least one author profile is performed for eachportion of content, of a plurality of portions of content, in the corpusof information, and wherein controlling the ingestion operation of theNLP system comprises prioritizing processing of portions of content, inthe plurality of portions of content, of the corpus of information basedon corresponding determined levels of matching associated with theportions of content, wherein first portions of content having relativelyhigher levels of matching than second portions of content in the corpusof information are ingested prior to the second portions of content. 3.The method of claim 2, wherein the prioritizing is performed based onuser specified priorities assigned to each of the author profiles in theat least one author profile and the determined levels of matching of theportions of content with each of the author profiles in the at least oneauthor profile, wherein at least two of the author profiles in the atleast one author profile have non-zero priorities different from oneanother.
 4. The method of claim 1, wherein each author profile in the atleast one author profile specifies characteristics of either aparticular individual subject matter expert author or a group of subjectmatter expert authors having at least one similar characteristic.
 5. Themethod of claim 1, wherein the NLP system is a question and answer (QA)system, and wherein the ingestion operation comprises ingestion ofdocuments from the corpus of information, for generating candidateanswers for a question input by a user, based on the user input.
 6. Themethod of claim 5, wherein controlling the ingestion operation of theNLP system comprises: ingesting a first sub-set of documents from thecorpus of information based on a relative priority of a first authorprofile associated with the first sub-set of documents compared topriorities of other author profiles in the at least one author profile;performing a QA system operation of the QA system on the first sub-setof documents to generate a first candidate answer to an input question;determining whether the first candidate answer has a correspondingconfidence score equal to or above a threshold confidence score; and inresponse to the corresponding confidence score not being equal to orabove the threshold confidence score: ingesting a second sub-set ofdocuments from the corpus of information associated with a second authorprofile having a relatively lower priority than the first authorprofile; and performing the QA system operation on the second sub-set ofdocuments to generate a second candidate answer to the input question.7. The method of claim 6, wherein ingesting the second sub-set ofdocuments comprises adding the second sub-set of documents to the firstsub-set of documents, and wherein performing the QA system operation onthe second sub-set of documents comprises performing the QA systemoperation on the combination of the first sub-set of documents and thesecond sub-set of documents, and wherein performing the QA systemoperation comprises modifying weights applied by the QA system togenerate confidence scores for candidate answers based on relativepriorities associated with the at least one user selected authorprofiles.
 8. The method of claim 1, wherein the at least one authorprofile comprises a plurality of author profiles, and wherein eachauthor profile in the plurality of author profiles has an associateduser specified priority, and wherein controlling the ingestion operationof the NLP system comprises ingesting first portions of content of thecorpus of information having associated author profiles with associatedhigher user specified priorities prior to ingesting second portions ofcontent of the corpus of information associated with author profileshaving associated lower user specified priorities, and wherein thesecond portions of content are ingested only in response to the NLPoperation performed by the NLP system based on the ingested firstportions of content generating results that do not meet a predeterminedcriterion.
 9. The method of claim 1, wherein controlling the ingestionoperation of the NLP system, comprises: ingesting first content,associated with a first user selected author profile having anassociated first user specified priority value associated with the firstuser selected author profile, from the corpus into the NLP system andperforming an NLP operation on the first content; and ingesting secondcontent, associated with a second user selected author profile having anassociated second user specified priority value associated with thesecond user selected author profile, after ingesting the first contentand performing the NLP operation on the first content, and performingthe NLP operation on the second content, wherein the first userspecified priority value indicates a higher priority associated with thefirst user selected author profile than the second user specifiedpriority value.
 10. The method of claim 1, wherein controlling theingestion operation of the NLP system further comprises: performing aconflict resolution operation in response to a portion of content in thecorpus of information being associated with more than one user selectedauthor profile in the at least one author profile, wherein the conflictresolution operation operates in favor of a highest priority valueassociated with user selected author profiles associated with theportion of content.
 11. A computer program product comprising anon-transitory computer readable medium having a computer readableprogram stored therein, wherein the computer readable program, whenexecuted on a computing device, causes the computing device to: receivea user input specifying at least one author profile indicating authorswhose content is to be ingested by a natural language processing systemto perform natural language processing (NLP) operations based oningested content; generate a content profile for a portion of content ofa corpus of information; compare the content profile to the at least oneauthor profile at least by, for each author profile in the at least oneauthor profile: comparing each of one or more first features extractedfrom the content profile to corresponding ones of one or more secondfeatures extracted from the author profile; calculating a degree ofmatching between the each of the one or more first features andcorresponding ones of the one or more second features to determine alevel of matching of the content profile with the author profile; andcomparing the level of matching with a predetermined threshold value;and control an ingestion operation of the NLP system, for ingestingcontent from the corpus of information into a memory data structure forprocessing by the NLP system, based on results of comparing the level ofmatching with the predetermined threshold, wherein in response toresults of the comparing of the level of matching with the predeterminedthreshold value indicating that the level of matching has a firstrelationship relative to the predetermined threshold, controlling theingestion operation comprises not ingesting, by the NLP system, theportion of content, and wherein in response to results of the comparingof the level of matching with the predetermined threshold valueindicating that the level of matching has a second relationship relativeto the predetermined threshold, controlling the ingestion operationcomprises ingesting the portion of content by the NLP system.
 12. Thecomputer program product of claim 11, wherein the computer readableprogram causes the computing device to perform the comparing of thecontent profile to the at least one author profile for each portion ofcontent, of a plurality of portions of content, in the corpus ofinformation, and wherein the computer readable program further causesthe computing device to control the ingestion operation of the NLPsystem at least by prioritizing processing of portions of content, inthe plurality of portions of content, of the corpus of information basedon corresponding determined levels of matching associated with theportions of content, wherein first portions of content having relativelyhigher levels of matching than second portions of content in the corpusof information are ingested prior to the second portions of content. 13.The computer program product of claim 12, wherein the prioritizing isperformed based on user specified priorities assigned to each of theauthor profiles in the at least one author profile and the determinedlevels of matching of the portions of content with each of the authorprofiles in the at least one author profile, wherein at least two of theauthor profiles in the at least one author profile have non-zeropriorities different from one another.
 14. The computer program productof claim 11, wherein each author profile in the at least one authorprofile specifies characteristics of either a particular individualsubject matter expert author or a group of subject matter expert authorshaving at least one similar characteristic.
 15. The computer programproduct of claim 11, wherein the NLP system is a question and answer(QA) system, and wherein the ingestion operation comprises ingestion ofdocuments from the corpus of information, for generating candidateanswers for a question input by a user, based on the user input.